Abstract
In this paper, I present an analysis of the depictive properties of deepfakes. These are videos and pictures produced by deep learning algorithms that automatically modify existing videos and photographs or generate new ones. I argue that deepfakes have an intentional standard of correctness. That is, a deepfake depicts its subject only insofar as its creator intends it to. This is due to the way in which these images are produced, which involves a degree of intentional control similar to that involved in the production of other intentional pictures such as drawings and paintings. This aspect distinguishes deepfakes from real videos and photographs, which instead have a non-intentional standard: their correct interpretation corresponds to the scenes that were recorded by the mechanisms that produced them, and not to what their producers intended them to represent. I show that these depictive properties make deepfakes fit for communicating information in the same way as language and other intentional pictures. That is, they do not provide direct access to the communicated information like non-intentional pictures (e.g., videos and photographs) do. Rather, they convey information indirectly, relying only on the viewer's assumptions about the communicative intentions of the creator of the deepfake. Indirect communication is indeed a prominent function of such media in our society, but it is often overlooked in the philosophical literature. This analysis also explains what is epistemically worrying about deepfakes. With the introduction of this technology, viewers interpreting photorealistic videos and pictures can no longer be sure of which standard to select (i.e., intentional or non-intentional), which makes misinterpretation likely (i.e., they can take an image to have a standard of correctness that it does not have).
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Notes
However, in Sect. 3 I show how the claims contained in this paper can be extended to other types of synthetic media such as DALL-E based images in interesting ways.
I do not need to side with any specific account of pictorial experience here. Indeed, both experiential and recognitional accounts generally agree on the need for the notion of a standard of correctness in theories of depiction (Terrone, 2021).
Millière (2022) provides a similar distinction between local and global deep learning-based media to distinguish algorithms that involve the modification of pre-existing, archival media, and algorithms that involve only the synthetization of new content. The main aspect under which my distinction and his differ is that mine takes into account the distribution of standards of correctness.
I thank an anonymous reviewer for highlighting this point.
It is beyond the scope of this paper to offer an exhaustive list of such relevant features. However, note that much work in computer science is currently devoted to the development of detection systems that can detect deepfakes. This is an example of a means that can be implemented for the preservation of videos as evidence, and therefore for the creation of epistemic channels.
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Acknowledgements
I am grateful to Roberto Casati for his comments on an early draft. I also would like to thank two anonymous referees for their helpful feedback which substantially improved this paper.
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Pierini, F. Deepfakes and depiction: from evidence to communication. Synthese 201, 97 (2023). https://doi.org/10.1007/s11229-023-04093-7
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DOI: https://doi.org/10.1007/s11229-023-04093-7